Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “engine design optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Eigenfrequency constrained topology optimization of finite strain hyperelastic structures

This paper incorporates hyperelastic materials, nonlinear kinematics, and preloads in eigenfrequency constrained density–based topology optimization. The formulation allows for initial finite deformations and subsequent small harmonic oscillations. The optimization problem is solved by the method of moving asymptotes, and the gradients are calculated using the adjoint method. Both simple and degenerate eigenfrequencies are considered in the sensitivity analysis. A well-posed topology optimization problem is formulated by filtering the volume fraction field. Numerical issues associated with excessive distortion and spurious eigenmodes in void regions are reduced by removing low volume fraction elements. The optimization objective is to maximize stiffness subject to a lower bound on the fundamental eigenfrequency. Numerical examples show that the eigenfrequencies drastically change with the load magnitude, and that the optimization is able to produce designs with the desired fundamental eigenfrequency.

42 ENGINEERING↗
DOE OSTI2020

Global kinetic modeling of NH 3 -SCR with two sites of NH 3 storage on Cu-SSZ-13

A comprehensive selective catalytic reduction (SCR) model is developed to detail the global kinetic reactions in the catalyst which is involved in two type of sites for high- and low-temperature NH 3 adsorption/desorption, as well as a low temperature H 2 O storage. The model accounts for the formation of ammonium nitrate in fast SCR and the decomposition formation of ammonium nitrate in the absence of NO, and addresses N 2 O relative to NO and NO 2 reactions with NH 3 . The model has been validated against commercial Cu-SSZ-13 measurements of NH 3 inventory, NH 3 oxidation, NO oxidation, standard SCR, fast SCR and NO 2 SCR at both comprehensive steady-state and transient conditions. In addition, the application of the SCR model for emissions control over a transient drive cycle at cold start was demonstrated based on diesel oxidation catalyst (DOC) and SCR aftertreatment system for a passenger car. In conclusion, the results show that the SCR model can provide the detailed emissions estimations for steady-state and transient conditions, can assist with understanding the impact of the SCR reaction mechanism, and can assist with the design and optimization of SCR catalysts to develop innovative technologies for co-optimization of engine fuel economy and emissions control technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Effect of water droplet growth dynamics on electrode current in fuel-cell catalyst layers

Fuel cells are a promising next-generation energy-conversion technology designed to replace internal combustion engines in transportation applications. However, much work remains to optimize them. Operation at high humidities causes liquid water droplet formation on Pt catalyst particles during oxygen reduction, potentially impeding reactant arrival to the reactive electrode. In this work, four different cases of water droplet growth in fuel-cell catalyst layers are considered: pinned or advancing droplets on a bare Pt surface, advancing droplets on a Nafion film, and water-layer growth in carbon nanopores. Transient drop growth is captured with a combination of mass, species mass, and momentum balances, and the subsequent limiting current is determined via oxygen diffusion and Tafel kinetics. Further, water droplets are found not to be mass-transfer limiting due to the relatively large liquid-gas area compared to the Pt nanoparticle. Mass-transfer-limited behavior is calculated in carbon nanopores.

25 ENERGY STORAGE↗
DOE OSTI2026

Structured for success: conjugated polymer binders with tailored composition and architecture for lithium-ion batteries

Conjugated polymer binders are replacing conventional binders in lithium-ion batteries. Herein, we examine how molecular engineering and hierarchical nanostructuring govern binder functionality and electrochemical performance. Lithium-ion batteries (LIBs) are the leading energy storage technology, yet enhancing their energy density and cycle life remains critical. Significant progress has been made in high-capacity anodes and high-voltage cathodes, but their performance is hindered by electrode degradation, where it is related to the behaviors of binders at the surface and interface. Conventional non-conductive binders like poly(vinylidene difluoride) (PVDF), combined with conductive additives, often fail to maintain electrical pathways under repeated volume changes. Alternatively, conjugated polymer binders have emerged as a superior alternative, simultaneously offering intrinsic conductivity, mechanical flexibility, and strong adhesion through π-conjugated backbones and functional groups. Their tunable molecular structure enables efficient electron/ion transport while mitigating electrode cracking. Additionally, the development of hierarchically ordered nanostructures in conjugated polymer binder can further enhance their electrochemical performance. This review examines the design principles of conjugated polymer binders, focusing on molecular engineering and nanostructural control to optimize their performance in high-loading electrodes, such as silicon-based anodes. By addressing key challenges in binder functionality, these advanced materials pave the way for next-generation high-energy-density LIBs.

Jin, Xiuyu↗
DOE OSTI2020

Applying Dynamic Strain on Thin Oxide Films Immobilized on a Pseudoelastic Nickel-Titanium Alloy

Direct alteration of materials structure/function through strain is a burgeoning area of research that has allowed for novel properties of materials to emerge. Tuning materials structure can be achieved by controlling an external force imposed on materials and inducing stress-strain responses (i.e., applying dynamic strain). Electroactive thin films are typically deposited on shape or volume tunable elastic substrates, where mechanical loading (i.e. compression or tension) can affect films structure and function through imposed strain. Here we summarize methods for straining n-type doped titanium dioxide (TiO2) films prepared by a thermal treatment of a pseudo-elastic nickel-titanium alloy. The main purpose of the described methods is to study how strain affects electrocatalytic activities of metal oxide, specifically hydrogen evolution and oxygen evolution reactions. The same system can be adapted to study the effect of strain more broadly. Strain engineering can be applied for optimization of a material function, as well as for design of adjustable, multifunctional (photo)electrocatalytic materials under external stress control.

92 SC - Basic Energy Sciences (BES) (SC-22)↗
DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗
DOE OSTI2025

Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Commonly produced as a byproduct of uranium fission, 99 Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k eff ) and natural uranium fission power, which are directly correlated with the amount of 99 Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

Accelerator-driven systems↗
DOE OSTI2022

Topology optimization for the design of porous electrodes

Porous electrodes are an integral part of many electrochemical devices since they have high porosity to maximize electrochemical transport and high surface area to maximize activity. Traditional porous electrode materials are typically homogeneous, stochastic collections of small-scale particles and offer few opportunities to engineer higher performance. Fortunately, recent breakthroughs in advanced and additive manufacturing are yielding new methods to structure and pattern porous electrodes across length scales. These architected electrodes are emerging as a promising new technology to continue to drive improvement; however, it is still unclear which structures to employ and few tools are available to guide their design. Here we address this gap by applying topology optimization to the design of porous electrodes. We demonstrate our framework on two applications: a porous electrode driving a steady Faradaic reaction and a transiently operated electrode in a supercapacitor. We present computationally designed electrodes that minimize energy losses in a half-cell. For low-conductivity materials, the optimization algorithm creates electrode designs with a hierarchy of length scales. Further, the designed electrodes are found to outperform undesigned, homogeneous electrodes. Finally, we present three-dimensional porous electrode designs. We thus establish a topology optimization framework for designing porous electrodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Stress‐constrained topology optimization of structures subjected to nonproportional loading

Abstract This work considers the topology optimization of hyperelastic structures for maximum stiffness (minimum compliance) subject to constraints on their volume and maximum stress. In contrast to almost all previous works, we subject the structures to nonproportional loading, wherein the maximum stress does not necessarily occur at the final load step. As such, the stress is constrained at each load step. The augmented Lagrangian method is used to formulate the optimization problem with its many constraints. In numerical examples, we investigate different load trajectories for the same terminal load and compare the optimized designs and their performances. The results show the importance of considering the entire load trajectory as the load history significantly influences the optimized designs.

42 ENGINEERING↗
DOE OSTI2023

Functionally Designed Ultra-Lightweight Carbon Fiber Reinforced Thermoplastic Composites Door Assembly (Final Scientific/Technical Report)

This is the final technical report for a project that developed an ultra-lightweight carbon fiber reinforced thermoplastics composite door assembly for a mid-size SUV. The project was able achieve a weight reduction of at least 42.5% while maintaining or exceeding the fit, function, safety, stiffness, and crash performance requirements of the current baseline door. Additionally, the target cost was set at less than $5 per pound of weight saved. To accomplish these goals, the project team employed a systems approach that involves redesigning the door assembly, exploring novel materials, optimizing the design, and utilizing advanced manufacturing technologies.

42 ENGINEERING↗
DOE OSTI2022

Floating Wind Turbine Control Optimization

We present a framework for optimizing the control parameters of floating offshore wind turbines (FOWTs). The framework combines aeroelastic simulations with a systems engineering model and control software. In an example of the optimization framework, we minimize tower damage equivalent loading with generator speed constraints. We also study the effect of thrust-limiting control and quantify the trade off between fatigue loading and energy capture using a set of optimal controller designs. Finally, we optimize the controller of four different FOWT models and compare their dynamic responses. Additional details and other use cases for the framework are presented, which can optimize different control problems and evaluate FOWT designs.

controls↗
DOE OSTI2024

Multiphysics Design Optimization and Additive Manufacturing of Nuclear Components (Final CRADA Report - Executive Summary)

Westinghouse Electric Company (WEC) actively participated in the advancement of the nuclear fuel and reactor design space and requested the help of Oak Ridge National Laboratory (ORNL) in the creation of a new design tool set. This report details the creation of a collection of software tool sets that are linked together to collectively assist WEC design engineers in developing novel ideas outside the normal scope of traditional nuclear fuel and reactor design formulas. Specifically, Siemens HEEDS, a design space exploration and parametric optimization software, monitored and changed parameters in a collection of softwares to meet the team’s objective. The HEEDS parametric optimization method, SHERPA, was developed to control the Siemens NX CAD platform to adjust the native CAD of a hexahedral spacer grid. This new geometry can be used to execute a topological design optimization by the NX Topology software add-in. The resulting geometry is additively manufacturable. This topological optimization occurred twice—once on the spacer grid’s spring, and once on the dimple geometry. These new geometries were imported by Siemens’ STAR-CCM+, a multiphysics structural and fluid dynamic computational solver in which the spring geometry is deflected to match the rod insertion configuration. Along with the dimple geometry, this new deflected spring was used to complete a hydraulic assessment of a single-unit cell comprising one rod, one spring, and two dimples. The HEEDS SHERPA algorithm ranks the design based on the final mass of the unit cell and the hydraulic pressure drop performance. The ORNL team demonstrated the ability to use this software and provided engineering judgement to apply modern aerospace aerodynamic design. The effort has been focused on thinking outside the conventional design space and redesigning a spacer grid to perform beyond the WEC set objectives. Furthermore, the ORNL team also demonstrated that the HEEDS optimization routine can independently develop a design that meets the WEC design goals. Although these designs were at a low technology readiness level, their demonstration confirmed the team’s capability to create novel advanced nuclear concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems

Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

An Integrated Computational Materials Engineering (ICME) Approach to Design Nonlinear Transition Zones Between Dissimilar Metals

Current approaches to designing graded transition joints (GTJs) between dissimilar metals often rely on linear changes in both composition profiles and thickness of each sublayer. This increases fabrication cost and may not be optimal with respect to residual stress or the formation of undesirable phases. Here, in this study, GTJs between P91 ferritic/martensitic steel and 347H austenitic stainless steel were designed using Integrated Computational Materials Engineering (ICME) principles with nonlinear composition and length profiles. Guided by inputs from classical mechanics and CALPHAD predictions of carbon chemical potential, a novel transition zone consisting of five discrete compositions was proposed, with the thickness of each sublayer varying according to a brachistochrone-inspired distribution. In addition to carbon potential gradients, CALPHAD was used to predict coefficients of thermal expansion, which were incorporated into finite element models to evaluate stress evolution. The proposed nonlinear design resulted in a smoother carbon potential gradient, lower carbon depletion at the P91 interface, and a comparable residual stress under long-term thermal exposure, compared to a conventional linear design using ten sublayers with equal thickness. This work introduces a brachistochrone-inspired distribution for GTJ design, offering a general framework for optimizing graded interfaces between dissimilar metals.

Directed Energy Deposition↗
DOE OSTI2022

Hydrogen Energy Storage Integrated with a Combined Cycle Plant

A project is being developed that will build upon the existing infrastructure and resources at the Intermountain Power Project (IPP) site to provide reliable, dispatchable energy and to support the transmission of renewable energy resources while transitioning to an economical green energy future. The concept study depicted in this report outlines a techno-economic optimization to fulfill the demand for 30% vol hydrogen co-firing in the IPP 840 MW advanced class combined cycle power plant. In an initial step, a site assessment concluded the site has sufficient land available to co-locate a hydrogen production and storage facility. The team evaluated and defined a scalable concept that considered technology characteristics, including input and output models to be used for optimization purposes. The concept for the hydrogen production and storage system integrates multiple technologies, to determine system size and scalable approach, for each of the technologies evaluated, the team defined component and subcomponent sizes, minimum and maximum capacity, modularity, component utility consumption (electric, water), component flexibility and servicing, layout, and technology status, as well as technology alternatives. For hydrogen generation, the project considers Siemens Energy’s Silyzer-300 (S300) technology, a 17.5 MW modular Proton Exchange Membrane (PEM) electrolyzer. For the S300 configuration, the team determined that three S300 arrays, or approximately 1,000 kg/hr, per block would yield a compact block design. This configuration results in a fairly wide and flexible arrangement that fits well into the spaces available at the site. Therefore, the overall design approach is based on multiple identical blocks of 3 arrays to minimize engineering cost and optimize constructability. In parallel, a transmission screening study was conducted to determine any potential transmission constraints from the energy sources that could feed the hydrogen production equipment. The study results show that minimum transmission constraints would be encountered to deliver 400 MW renewable generation from southern California, or south-central Wyoming. At last, the techno-economic analysis concluded that a scenario that uses solar and wind power yields the lowest levelized cost of hydrogen (LCOH 2 ) production and the lowest cost per tonne of CO 2 reduced. In this optimized scenario, the hydrogen production plant was determined as 6,201 kg/hr and the hydrogen storage (underground cavern) was determined as 4,600 tonnes. The resulting capacity factor for the hydrogen production plant was 66.33% with 8,745 operating hours in one year. This techno-economic analysis provided various options for integrating hydrogen storage at the Intermountain Power Plant site to co-fire the CCPP units. The results provide insightful data about the magnitude of capacity needed and the economics of producing hydrogen and reducing CO 2 emissions.

08 HYDROGEN↗
DOE OSTI2025

Optimizing qubit control pulses for state preparation

In the burgeoning field of quantum computing, the precise design and optimization of quantum pulses are essential for enhancing qubit operation fidelity. This study focuses on refining the pulse engineering techniques for superconducting qubits, employing a detailed analysis of square and Gaussian pulse envelopes under various approximation schemes. We evaluated the effects of coherent errors induced by naive pulse designs. Furthermore, we identified the sources of these errors in the Hamiltonian model’s approximation level. We mitigated these errors through adjustments to the external driving frequency and pulse durations, thus implementing a pulse scheme with stroboscopic error reduction. Our results demonstrate that these refined pulse strategies improve performance and reduce coherent errors. Moreover, the techniques developed herein are applicable across different quantum architectures, such as ion-trap, atomic, and photonic systems.

Chirp modulation↗
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Design optimization of integrated cooling inserts in modular Fischer-Tropsch reactors

Sustainable production of liquid fuels and feedstocks from atmospheric CO 2 through carbon recycling technologies is necessary to broaden decarbonization efforts and further reduce global emissions. Fischer–Tropsch (FT) reactors can address this challenge by providing storable, high-value liquid hydrocarbon fuels and feedstocks from syngas generated through reductive CO 2 utilization. FT technology, however, is most cost effective at large-scale, fixed-site plants and does not effectively address the smaller, globally distributed CO 2 point sources. Alternatively, smaller, modular reactors can be composed together to the specific scale of the emission source, yielding a flexible solution that enables more widespread deployment. In these modular reactors, thermal management using a finned cooling insert embedded within the catalyst matrix is critical to maintaining the performance and viability. Thus, in this work, topology optimization is used to determine the cooling insert geometry that maximizes reactor productivity while preventing auto-thermal runaway. Optimal designs are generated for varying number of constituent fins and over a range of maximum operating temperatures. Constraints including minimum feature length-scales and prescribed cooling insert material are imposed on the designs to enhance manufacturability. The impact of design features such as insert tapering and increased length scale hierarchy is automatically revealed by the systematic design framework employed. Here, this approach thus generates novel optimal geometries while automating, accelerating, and enhancing the design process compared to traditional heuristic approaches for cooling insert design in modular FT reactors.

30 DIRECT ENERGY CONVERSION↗